Mobile app data provides accurate spatial and temporal details to present human trajectories. Recognising the challenges posed by the inherent variability and large volume of data, the research refines data processing techniques through the application of sensitivity analysis, explicitly using distance parameters in activity detection. The mobility indicator, activity disintegration (AD), is introduced to present the effectiveness of the model using sequential, spatial, and temporal search from individual mobile app trajectories. The results demonstrate how AD varies in different thresholds and cities in urban mobility analysis.